Misalignment resilient CCA for interactive satellite image change detection

摘要

Change detection, in multi-temporal satellite imagery, seeks to discover relevant changes and to discard irrelevant ones. This task is usually achieved by modeling accurate decision criteria that capture the user's intention while being resilient to many irrelevant changes including acquisition conditions. Among existing change detection solutions, correlation-based models - such as canonical correlation analysis (CCA) - are particularly successful, but their success is very dependent on the quality of alignments used to train these models. In this paper, we introduce a novel interactive change detection algorithm based on a new variant of CCA, referred to as misalignment resilient CCA. Given a small sample of “changes” and “no-changes” labeled by an oracle (user), our method learns transformation matrices that map these data from different input spaces, related to multi-temporal images, into a common latent space which is sensitive to relevant changes while being resilient to irrelevant ones including misalignments. These CCA transformations correspond to the optimum of a particular constrained maximization problem that mixes a new soft-alignment term and a context-based regularization criterion. Extensive experiments conducted in interactive satellite image change detection, show that our misalignment resilient CCA approach is highly effective.